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review.mlplatform.org / ml / ComputeLibrary / e8f28136a048829211d30208cdd6679f0f1c1632 / . / arm_compute / runtime / CL / functions / CLQLSTMLayer.h
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Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]1/*
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]2 * Copyright (c) 2020-2021 Arm Limited.
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24#ifndef ARM_COMPUTE_CLQLSTMLAYER_H
25#define ARM_COMPUTE_CLQLSTMLAYER_H
26
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]27#include "arm_compute/core/Types.h"
28#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]29#include "arm_compute/runtime/CL/functions/CLCopy.h"
Michalis Spyrouad7515d2020-07-24 00:02:23 +0100[diff] [blame]30#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h"
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]31#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
32#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
Michalis Spyrou1009e872020-07-27 12:48:34 +0100[diff] [blame]33#include "arm_compute/runtime/CL/functions/CLPixelWiseMultiplication.h"
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]34#include "arm_compute/runtime/CL/functions/CLTranspose.h"
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]35#include "arm_compute/runtime/common/LSTMParams.h"
36
37namespace arm_compute
38{
39// Forward declarations
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]40class CLCompileContext;
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]41class ICLTensor;
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]42class CLQLSTMLayerNormalizationKernel;
43class ITensorInfo;
Georgios Pinitas4a578b92021-06-25 12:13:49 +0100[diff] [blame]44namespace opencl
45{
46namespace kernels
47{
48class ClGemmLowpMatrixAReductionKernel;
49} // namespace kernels
50} // namespace opencl
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]51
52/** Basic function to run @ref CLQLSTMLayer
53 *
54 * This function calls the following CL functions/kernels:
55 *
56 * -# @ref CLActivationLayer Activation functions (tanh and logistic)
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]57 * -# @ref CLCopy Copy function for copying output_state_out to output
58 * -# @ref CLArithmeticAddition Elementwise addition and subtraction
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]59 * -# @ref CLGEMMLowpMatrixMultiplyCore Quantized matrix multiplication core. Accumulators are 32-bit integers
Georgios Pinitas4a578b92021-06-25 12:13:49 +0100[diff] [blame]60 * -# @ref CLGEMMLowpOutputStage Convert 32-bit integers into QSYMM16
61 * -# @ref opencl::kernels::ClGemmLowpMatrixAReductionKernel For precomputing effective biases to use
Sheri Zhang7e20e292021-02-02 11:49:34 +0000[diff] [blame]62 * -# @ref CLPixelWiseMultiplication Elementwise multiplication
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]63 * -# @ref CLTranspose Transpose function for reshaping the weights
64 * */
65class CLQLSTMLayer : public IFunction
66{
67public:
68 /** Default constructor */
69 CLQLSTMLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
70 /** Prevent instances of this class from being copied (As this class contains pointers) */
71 CLQLSTMLayer(const CLQLSTMLayer &) = delete;
72 /** Default move constructor */
73 CLQLSTMLayer(CLQLSTMLayer &&) = default;
74 /** Prevent instances of this class from being copied (As this class contains pointers) */
75 CLQLSTMLayer &operator=(const CLQLSTMLayer &) = delete;
76 /** Default move assignment operator */
77 CLQLSTMLayer &operator=(CLQLSTMLayer &&) = default;
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]78 /** Default destructor */
79 ~CLQLSTMLayer();
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]80 /** Initialize function's tensors.
81 *
Teresa Charlin62687422021-04-28 10:58:49 +0100[diff] [blame]82 * Valid data layouts:
83 * - All
84 *
85 * Valid data type configurations:
86 * |src0 |src1 - src6 |src7 -src9 |src10 |src11 |dst0 |dst1 - dst2 |
87 * |:-------------|:------------|:------------|:------|:-------------|:------|:-----------------|
88 * |QASYMM8_SIGNED|QASYMM8 |S32 |QSYMM16|QASYMM8_SIGNED|QSYMM16|QASYMM8_SIGNED |
89 *
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]90 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
91 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
92 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
93 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
94 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
95 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
96 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
97 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
98 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
99 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
Michele Di Giorgiobeb2d452020-05-11 16:17:51 +0100[diff] [blame]100 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
101 * @param[in] output_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
102 * @param[out] cell_state_out Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
103 * @param[out] output_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
104 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]105 * @param[in] lstm_params Weights tensors used in peephole, CIFG and layer normalization optimizations:
106 * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
107 * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
108 * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
109 * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
110 * hidden_state_zero The zero point of the hidden state.
111 * hidden_state_scale The scale of the hidden state.
112 * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
113 * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
114 * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
115 * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
116 * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
117 * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
118 * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
119 * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32.
120 * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
121 * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
122 * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
123 * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
124 * cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
125 * If set to 0.0 then clipping is disabled.
126 * projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
127 * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
128 */
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]129 void configure(const ICLTensor *input,
130 const ICLTensor *input_to_forget_weights,
131 const ICLTensor *input_to_cell_weights,
132 const ICLTensor *input_to_output_weights,
133 const ICLTensor *recurrent_to_forget_weights,
134 const ICLTensor *recurrent_to_cell_weights,
135 const ICLTensor *recurrent_to_output_weights,
136 const ICLTensor *forget_gate_bias,
137 const ICLTensor *cell_bias,
138 const ICLTensor *output_gate_bias,
139 ICLTensor *cell_state_in,
140 ICLTensor *output_state_in,
141 ICLTensor *cell_state_out,
142 ICLTensor *output_state_out,
143 ICLTensor *output,
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]144 const LSTMParams<ICLTensor> &lstm_params);
145
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]146 /** Initialize function's tensors.
147 *
148 * @param[in] compile_context The compile context to be used.
149 * @param[in] input Source tensor. Input is a 2D tensor with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
150 * @param[in] input_to_forget_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
151 * @param[in] input_to_cell_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
152 * @param[in] input_to_output_weights 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
153 * @param[in] recurrent_to_forget_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
154 * @param[in] recurrent_to_cell_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
155 * @param[in] recurrent_to_output_weights 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
156 * @param[in] forget_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
157 * @param[in] cell_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
158 * @param[in] output_gate_bias 1D weights tensor with dimensions [num_units]. Data type supported: S32.
Michele Di Giorgiobeb2d452020-05-11 16:17:51 +0100[diff] [blame]159 * @param[in] cell_state_in 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
160 * @param[in] output_state_in 2D tensor with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
161 * @param[out] cell_state_out Destination tensor. Output is a 2D tensor with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
162 * @param[out] output_state_out Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
163 * @param[out] output Destination tensor. Output is a 2D tensor with dimensions [output_size, batch_size].Data types supported: Same as @p input.
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]164 * @param[in] lstm_params Weights tensors used in peephole, CIFG and layer normalization optimizations:
165 * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
166 * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
167 * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
168 * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
169 * hidden_state_zero The zero point of the hidden state.
170 * hidden_state_scale The scale of the hidden state.
171 * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
172 * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
173 * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
174 * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
175 * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
176 * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
177 * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
178 * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32.
179 * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
180 * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
181 * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
182 * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
183 * cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
184 * If set to 0.0 then clipping is disabled.
185 * projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
186 * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
187 */
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]188 void configure(const CLCompileContext &compile_context,
189 const ICLTensor *input,
190 const ICLTensor *input_to_forget_weights,
191 const ICLTensor *input_to_cell_weights,
192 const ICLTensor *input_to_output_weights,
193 const ICLTensor *recurrent_to_forget_weights,
194 const ICLTensor *recurrent_to_cell_weights,
195 const ICLTensor *recurrent_to_output_weights,
196 const ICLTensor *forget_gate_bias,
197 const ICLTensor *cell_bias,
198 const ICLTensor *output_gate_bias,
199 ICLTensor *cell_state_in,
200 ICLTensor *output_state_in,
201 ICLTensor *cell_state_out,
202 ICLTensor *output_state_out,
203 ICLTensor *output,
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]204 const LSTMParams<ICLTensor> &lstm_params);
205
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]206 /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer
207 *
Michele Di Giorgiobeb2d452020-05-11 16:17:51 +0100[diff] [blame]208 * @param[in] input Source tensor info. Input is a 2D tensor info with dimensions [input_size, batch_size]. Data types supported: QASYMM8_SIGNED.
209 * @param[in] input_to_forget_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
210 * @param[in] input_to_cell_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
211 * @param[in] input_to_output_weights 2D weights tensor info with dimensions [input_size, num_units]. Data type supported: QSYMM8.
212 * @param[in] recurrent_to_forget_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
213 * @param[in] recurrent_to_cell_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
214 * @param[in] recurrent_to_output_weights 2D weights tensor info with dimensions [output_size, num_units]. Data type supported: QSYMM8.
215 * @param[in] forget_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32.
216 * @param[in] cell_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32.
217 * @param[in] output_gate_bias 1D weights tensor info with dimensions [num_units]. Data type supported: S32.
218 * @param[in] cell_state_in 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
219 * @param[in] output_state_in 2D tensor info with dimensions [output_size, batch_size]. Data type supported: Same as @p input.
220 * @param[in] cell_state_out Destination tensor info. Output is a 2D tensor info with dimensions [num_units, batch_size]. Data type supported: QSYMM16.
221 * @param[in] output_state_out Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
222 * @param[in] output Destination tensor info. Output is a 2D tensor info with dimensions [output_size, batch_size].Data types supported: Same as @p input.
223 * @param[in] lstm_params Weights tensors info used in peephole, CIFG and layer normalization optimizations:
224 * input_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
225 * forget_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
226 * cell_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
227 * output_intermediate_scale Scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
228 * hidden_state_zero The zero point of the hidden state.
229 * hidden_state_scale The scale of the hidden state.
230 * input_to_input_weights (Optional) 2D weights tensor with dimensions [input_size, num_units]. Data type supported: QSYMM8.
231 * recurrent_to_input_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
232 * cell_to_input_weights (Optional) 1D weights tensor with dimensions [num_units]. Can be nullptr. Data type supported: QSYMM16.
233 * cell_to_forget_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
234 * cell_to_output_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
235 * input_gate_bias (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: S32.
236 * projection_weights (Optional) 2D weights tensor with dimensions [output_size, num_units]. Data type supported: QSYMM8.
237 * projection_bias (Optional) 1D weights tensor with dimensions [output_size]. S32.
238 * input_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
239 * forget_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
240 * cell_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
241 * output_layer_norm_weights (Optional) 1D weights tensor with dimensions [num_units]. Data type supported: QSYMM16.
242 * cell_threshold (Optional) The clipping threshold for the cell state, such that values are bound within [-cell_clip, cell_clip].
243 * If set to 0.0 then clipping is disabled.
244 * projection_threshold (Optional) The clipping threshold for the output from the projection layer, such that values are bound within
245 * [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]246 * @return a status
247 */
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]248 static Status validate(const ITensorInfo *input,
249 const ITensorInfo *input_to_forget_weights,
250 const ITensorInfo *input_to_cell_weights,
251 const ITensorInfo *input_to_output_weights,
252 const ITensorInfo *recurrent_to_forget_weights,
253 const ITensorInfo *recurrent_to_cell_weights,
254 const ITensorInfo *recurrent_to_output_weights,
255 const ITensorInfo *forget_gate_bias,
256 const ITensorInfo *cell_bias,
257 const ITensorInfo *output_gate_bias,
258 const ITensorInfo *cell_state_in,
259 const ITensorInfo *output_state_in,
260 const ITensorInfo *cell_state_out,
261 const ITensorInfo *output_state_out,
262 const ITensorInfo *output,
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]263 const LSTMParams<ITensorInfo> &lstm_params);
264
265 // Inherited methods overridden:
266 void run() override;
267 void prepare() override;
268
269private:
Sheri Zhang3a353982020-04-21 13:10:24 +0100[diff] [blame]270 enum class LayerNormGate : uint8_t
271 {
272 Forget,
273 Cell,
274 Input,
275 Output,
276 Count
277 };
Sang-Hoon Parka7431ae2020-05-12 11:13:30 +0100[diff] [blame]278 static constexpr uint8_t _layer_norm_count = static_cast<uint8_t>(LayerNormGate::Count);
279 static constexpr uint32_t _out_state_output_size_dimension_idx = 0;
Sheri Zhang3a353982020-04-21 13:10:24 +0100[diff] [blame]280
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]281 /** Internal method to configure matrix multiplication plus output stage of each gate.
282 *
Manuel Bottini2b84be52020-04-08 10:15:51 +0100[diff] [blame]283 * @param[in] compile_context The compile context to be used.
284 * @param[in] mm Matrix multiplication function to use.
285 * @param[in] outstage Output stage function to use.
286 * @param[in] gemmlowp_info GEMMLowp metadata to be used by the output stage.
287 * @param[in] mm_input Input tensor to matrix multiplication function.
288 * @param[in] mm_weights Weights tensor to matrix multiplication function.
289 * @param[in] bias Bias tensor to matrix multiplication function.
290 * @param[in] outstage_res Tensor to be used for storing the result of the output stage.
291 * @param[in] gemmlowp_scale Real multiplier to be used computing multiplier and shift for requantization.
292 * @param[in] mm_res_info Tensor info to be used to initialize matrix multiplication result tensor.
293 * @param[in] mm_res_info Tensor info to be used to initialize output stage result tensor.
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]294 *
295 */
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]296 void configure_mm(const CLCompileContext &compile_context,
297 CLGEMMLowpMatrixMultiplyCore &mm,
298 CLGEMMLowpOutputStage &outstage,
299 GEMMLowpOutputStageInfo &gemmlowp_info,
300 const ICLTensor *mm_input,
301 const ICLTensor *mm_weights,
302 const ICLTensor *bias,
303 CLTensor *mm_res,
304 CLTensor *outstage_res,
305 float gemmlowp_scale,
306 const TensorInfo &mm_res_info,
307 const TensorInfo &outstage_tensor_info);
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]308
309 MemoryGroup _memory_group{};
310
Sang-Hoon Parka7431ae2020-05-12 11:13:30 +0100[diff] [blame]311 /** A small internel kernel do the copy between two tensors */
312 class TensorCopyKernel
313 {
314 static constexpr uint32_t max_dimension_supported = 2;
315
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]316 ICLTensor *_src{nullptr};
317 ICLTensor *_dst{nullptr};
Sang-Hoon Parka7431ae2020-05-12 11:13:30 +0100[diff] [blame]318 size_t _row_size{};
319 Window _window{};
320
321 public:
322 /** Static function to check if given info will lead to a valid configuration of @ref CLQLSTMLayer::TensorCopyKernel
323 *
324 * @param[in] src Source tensor info.
325 * @param[in] dst Destination tensor info
326 *
327 * @return a status
328 */
329 static Status validate(const ITensorInfo &src, const ITensorInfo &dst);
330 /** Set the input and output tensors.
331 *
332 * @param[in] src Source tensor
333 * @param[out] dst Destination tensor
334 */
335 void configure(ICLTensor &src, ICLTensor &dst);
336 /** run the kernel */
337 void run();
338 };
339
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]340 // Functions used
Georgios Pinitas4a578b92021-06-25 12:13:49 +0100[diff] [blame]341 CLTranspose _transpose_input_to_forget_weights{};
342 CLTranspose _transpose_input_to_cell_weights{};
343 CLTranspose _transpose_input_to_output_weights{};
344 CLTranspose _transpose_input_to_input_weights{};
345 CLTranspose _transpose_recurrent_to_forget_weights{};
346 CLTranspose _transpose_recurrent_to_cell_weights{};
347 CLTranspose _transpose_recurrent_to_output_weights{};
348 CLTranspose _transpose_recurrent_to_input_weights{};
349 CLTranspose _transpose_projection_weights{};
350 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _input_to_input_reduction;
351 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _recurrent_to_input_reduction;
352 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _input_to_forget_reduction;
353 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _recurrent_to_forget_reduction;
354 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _input_to_cell_reduction;
355 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _recurrent_to_cell_reduction;
356 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _input_to_output_reduction;
357 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _recurrent_to_output_reduction;
358 std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel> _projection_reduction;
359 CLArithmeticAddition _projection_bias_add{};
360 CLGEMMLowpMatrixMultiplyCore _mm_input_to_forget{};
361 CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_forget{};
362 CLPixelWiseMultiplication _pixelwise_mul_cell_to_forget{};
363 CLGEMMLowpOutputStage _input_to_forget_outstage{};
364 CLGEMMLowpOutputStage _recurrent_to_forget_outstage{};
365 CLGEMMLowpOutputStage _cell_to_forget_outstage{};
366 CLArithmeticAddition _accumulate_input_recurrent_forget{};
367 CLArithmeticAddition _accumulate_cell_forget{};
368 CLActivationLayer _forget_gate_sigmoid{};
369 CLGEMMLowpMatrixMultiplyCore _mm_input_to_cell{};
370 CLGEMMLowpOutputStage _input_to_cell_outstage{};
371 CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_cell{};
372 CLGEMMLowpOutputStage _recurrent_to_cell_outstage{};
373 CLArithmeticAddition _accumulate_input_recurrent_modulation{};
374 CLActivationLayer _cell_gate_tanh{};
375 CLArithmeticSubtraction _input_gate_sub{};
376 CLGEMMLowpMatrixMultiplyCore _mm_input_to_input{};
377 CLGEMMLowpOutputStage _input_to_input_outstage{};
378 CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_input{};
379 CLGEMMLowpOutputStage _recurrent_to_input_outstage{};
380 CLArithmeticAddition _accumulate_input_recurrent_input{};
381 CLPixelWiseMultiplication _pixelwise_mul_cell_to_input{};
382 CLGEMMLowpOutputStage _cell_to_input_outstage{};
383 CLArithmeticAddition _accumulate_cell_input{};
384 CLActivationLayer _input_gate_sigmoid{};
385 CLPixelWiseMultiplication _pixelwise_mul_forget_cell{};
386 CLPixelWiseMultiplication _pixelwise_mul_input_cell{};
387 CLArithmeticAddition _add_forget_cell{};
388 CLActivationLayer _cell_clip{};
389 CLGEMMLowpMatrixMultiplyCore _mm_input_to_output{};
390 CLGEMMLowpOutputStage _input_to_output_outstage{};
391 CLGEMMLowpMatrixMultiplyCore _mm_recurrent_to_output{};
392 CLGEMMLowpOutputStage _recurrent_to_output_outstage{};
393 CLArithmeticAddition _accumulate_input_recurrent_output{};
394 CLPixelWiseMultiplication _pixelwise_mul_cell_to_output{};
395 CLGEMMLowpOutputStage _cell_to_output_outstage{};
396 CLArithmeticAddition _accumulate_cell_to_output{};
397 CLActivationLayer _output_gate_sigmoid{};
398 CLActivationLayer _hidden_tanh{};
399 CLPixelWiseMultiplication _pixelwise_mul_hidden{};
400 CLGEMMLowpOutputStage _hidden_outstage{};
401 CLGEMMLowpMatrixMultiplyCore _mm_projection{};
402 CLGEMMLowpOutputStage _projection_outstage{};
403 CLArithmeticAddition _accumulate_projection{};
404 CLActivationLayer _projection_clip{};
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]405 std::array<std::unique_ptr<CLQLSTMLayerNormalizationKernel>, _layer_norm_count> _layer_norms;
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]406 CLCopy _copy_output;
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]407
Sang-Hoon Parka7431ae2020-05-12 11:13:30 +0100[diff] [blame]408 TensorCopyKernel _projection_bias_copy{};
409 TensorCopyKernel _projection_output_to_accumulate_copy{};
410 TensorCopyKernel _projection_accumulate_to_output_copy{};
411 TensorCopyKernel _hidden_to_output_copy{};
412
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]413 // Tensor pointers
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]414 const ICLTensor *_input_to_input_weights{nullptr};
415 const ICLTensor *_recurrent_to_input_weights{nullptr};
416 const ICLTensor *_projection_bias{nullptr};
417 const ICLTensor *_input_to_forget_weights{nullptr};
418 const ICLTensor *_input_to_cell_weights{nullptr};
419 const ICLTensor *_input_to_output_weights{nullptr};
420 const ICLTensor *_recurrent_to_forget_weights{nullptr};
421 const ICLTensor *_recurrent_to_cell_weights{nullptr};
422 const ICLTensor *_recurrent_to_output_weights{nullptr};
423 const ICLTensor *_projection_weights{nullptr};
424 std::array<const ICLTensor *, _layer_norm_count> _layer_norm_weights{{}};
425 std::array<const ICLTensor *, _layer_norm_count> _layer_norm_bias{{}};
Sheri Zhang3a353982020-04-21 13:10:24 +0100[diff] [blame]426
427 using LayerNormIndexType = typename std::underlying_type<LayerNormGate>::type;
428 inline LayerNormIndexType getGateIndex(LayerNormGate g)
429 {
430 return static_cast<LayerNormIndexType>(g);
431 }
432
433 inline void set_layer_norm_weight(const ICLTensor *t, LayerNormGate g)
434 {
435 _layer_norm_weights[getGateIndex(g)] = t;
436 }
437
438 inline void set_layer_norm_bias(const ICLTensor *t, LayerNormGate g)
439 {
440 _layer_norm_bias[getGateIndex(g)] = t;
441 }
442
443 inline const ICLTensor *get_layer_norm_weight(LayerNormGate g)
444 {
445 return _layer_norm_weights[getGateIndex(g)];
446 }
447
448 inline const ICLTensor *get_layer_norm_bias(LayerNormGate g)
449 {
450 return _layer_norm_bias[getGateIndex(g)];
451 }
452
453 inline CLQLSTMLayerNormalizationKernel &get_layer_norm(LayerNormGate g)
454 {
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]455 return *_layer_norms[getGateIndex(g)];
Sheri Zhang3a353982020-04-21 13:10:24 +0100[diff] [blame]456 }
457
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]458 inline void configure_layer_norm(LayerNormGate g, const ICLTensor *in);
Sang-Hoon Parkbef7fa22020-10-21 15:58:54 +0100[diff] [blame]459 inline static Status validate_layer_norm(const ITensorInfo &in, const ITensorInfo &weight, const ITensorInfo &bias);
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]460
461 // Temporary tensors
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]462 CLTensor _input_to_forget_weights_transposed{nullptr};
463 CLTensor _input_to_cell_weights_transposed{nullptr};
464 CLTensor _input_to_output_weights_transposed{nullptr};
465 CLTensor _input_to_input_weights_transposed{nullptr};
466 CLTensor _recurrent_to_forget_weights_transposed{nullptr};
467 CLTensor _recurrent_to_cell_weights_transposed{nullptr};
468 CLTensor _recurrent_to_output_weights_transposed{nullptr};
469 CLTensor _recurrent_to_input_weights_transposed{nullptr};
470 CLTensor _projection_weights_transposed{nullptr};
471 CLTensor _input_to_input_eff_bias{nullptr};
472 CLTensor _recurrent_to_input_eff_bias{nullptr};
473 CLTensor _input_to_forget_eff_bias{nullptr};
474 CLTensor _recurrent_to_forget_eff_bias{nullptr};
475 CLTensor _input_to_cell_eff_bias{nullptr};
476 CLTensor _recurrent_to_cell_eff_bias{nullptr};
477 CLTensor _input_to_output_eff_bias{nullptr};
478 CLTensor _recurrent_to_output_eff_bias{nullptr};
479 CLTensor _projection_reduction_res{nullptr};
480 CLTensor _projection_eff_bias{nullptr};
481 CLTensor _mm_input_to_forget_res{nullptr};
482 CLTensor _mm_recurrent_to_forget_res{nullptr};
483 CLTensor _mul_cell_to_forget_res{nullptr};
484 CLTensor _input_to_forget_outstage_res{nullptr};
485 CLTensor _cell_to_forget_outstage_res{nullptr};
486 CLTensor _recurrent_to_forget_outstage_res{nullptr};
487 CLTensor _forget_gate{nullptr};
488 CLTensor _mm_input_to_cell_res{nullptr};
489 CLTensor _input_to_cell_outstage_res{nullptr};
490 CLTensor _mm_recurrent_to_cell_res{nullptr};
491 CLTensor _recurrent_to_cell_outstage_res{nullptr};
492 CLTensor _cell_gate{nullptr};
493 CLTensor _mul_input_cell_res{nullptr};
494 CLTensor _mm_input_to_input_res{nullptr};
495 CLTensor _input_to_input_outstage_res{nullptr};
496 CLTensor _mm_recurrent_to_input_res{nullptr};
497 CLTensor _mul_cell_to_input_res{nullptr};
498 CLTensor _cell_to_input_outstage_res{nullptr};
499 CLTensor _recurrent_to_input_outstage_res{nullptr};
500 CLTensor _input_gate{nullptr};
501 CLTensor _mm_input_to_output_res{nullptr};
502 CLTensor _input_to_output_outstage_res{nullptr};
503 CLTensor _mm_recurrent_to_output_res{nullptr};
504 CLTensor _mul_cell_to_output_res{nullptr};
505 CLTensor _cell_to_output_outstage_res{nullptr};
506 CLTensor _recurrent_to_output_outstage_res{nullptr};
507 CLTensor _output_gate{nullptr};
508 CLTensor _hidden_mul_res{nullptr};
509 CLTensor _hidden_gate{nullptr};
510 CLTensor _mm_projection_res{nullptr};
511 CLTensor _projection_outstage_res{nullptr};
512 CLTensor _projection_out_res{nullptr};
513 CLTensor _projection_accumulate_res{nullptr};
514 CLTensor _ones{nullptr};
515 std::array<CLTensor, _layer_norm_count> _layer_norm_output{{}};
Sheri Zhang3a353982020-04-21 13:10:24 +0100[diff] [blame]516
517 inline CLTensor &get_layer_norm_output(LayerNormGate g)
518 {
519 return _layer_norm_output[getGateIndex(g)];
520 }
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]521
Felix Thomasmathibalanafd38f02023-09-27 17:46:17 +0100[diff] [blame]522 bool _is_prepared{false};
523 bool _has_cifg{false};
524 bool _has_cell_clipping{false};
525 bool _has_projection{false};
526 bool _has_projection_clipping{false};
527 bool _has_peephole{false};
528 bool _has_layer_norm{false};
529 bool _projection_tensor_copy_required{false};
Michele Di Giorgio1c1b3aa2020-04-02 17:35:42 +0100[diff] [blame]530};
531} // namespace arm_compute
532#endif /* ARM_COMPUTE_CLQLSTMLAYER_H */